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Accurately predicting project revenues and understanding client engagement are mission-critical for digital media and communications agencies. Yet, teams often face data silos—quotes scattered across platforms, inconsistent service records, and missed signals to deepen key client relationships. This case study matters for any agency with project-based services: it reveals how agentic AI can instantly synthesize complex sales data, unlocking insights that drive both near-term closes and long-term, strategic growth.
Using Scoop, the agency gained immediate visibility into both the breadth and depth of its client relationships, project pipeline, and service popularity. Leadership could now quantify the impact of their diverse service offerings and pinpoint when and where revenue growth was likely to occur. Key clients—previously identified anecdotally—were confirmed to represent outsized revenue potential, and service demand trends were made explicit. Critically, operations could forecast not just overall pipeline but identify which sectors and services should be targeted for future growth. This level of automated synthesis and high-trust decision support was simply not accessible with manual spreadsheets or static BI dashboards.
Every row in the dataset represented a distinct proposal, confirming the agency’s quote process was comprehensive and free of duplicates.
The largest proposal identified underscored the agency’s ability to secure major projects and illustrated growth potential for enterprise-scale deals.
The largest proposal identified underscored the agency’s ability to secure major projects and illustrated growth potential for enterprise-scale deals.
The aggregate value of all quotes analyzed demonstrated the substantial sales pipeline managed by the agency’s team in the recent period.
The most active client accounted for several proposals, revealing a high-value, repeat relationship that could be proactively expanded.
Digital media and communications agencies regularly juggle dozens of client quotes, proposals, and projects, each spanning a variety of services such as video production, editorial consulting, and design. Typically, key data remains fragmented—stored within isolated sales tools or spreadsheets—limiting an agency's ability to accurately forecast revenue, identify high-value partnerships, and optimize their service portfolio. Without holistic analysis, operational leaders struggle to distinguish between repeat business, new opportunities, or gaps in service offerings. Traditional BI solutions require manual setup and lack the intelligence to spot nuanced patterns in deal timing, client segmentation, and cross-service demand. These issues often lead to missed upsell opportunities, underutilized client relationships, and delayed revenue recognition.
Scoop ingested a quote management dataset featuring 69 unique client proposals, each containing client identifiers, initial quote values, services proposed, and direct references to supporting documentation. This dataset covered a balanced range of project sizes—primarily between 1,000 and 10,000 in local currency—with some larger contracts exceeding 60,000. The service mix included video production, editorial consulting, design, and web development, enabling comprehensive insight into core and ancillary offerings.
Scoop’s autonomous data pipeline executed the following steps:
Scoop ingested a quote management dataset featuring 69 unique client proposals, each containing client identifiers, initial quote values, services proposed, and direct references to supporting documentation. This dataset covered a balanced range of project sizes—primarily between 1,000 and 10,000 in local currency—with some larger contracts exceeding 60,000. The service mix included video production, editorial consulting, design, and web development, enabling comprehensive insight into core and ancillary offerings.
Scoop’s autonomous data pipeline executed the following steps:
Scoop’s agentic approach surfaced trends invisible to first-glance reporting. The system highlighted that most business was not only clustered in the moderate project value band (1,000–10,000) but also spiked ahead of key calendar periods—specifically, a concentration of high-value quotes set for early next year. Routine BI tools tend to flatten time-based spikes or dilute them in quarterly aggregates; Scoop’s temporal pattern tracing made such surges unmistakable. The solution further revealed that a few key clients—previously assumed to be only moderately engaged—were in fact driving substantial and recurring revenue, suggesting immediate opportunities for targeted retention and cross-sell campaigns. Demand segmentation by service type also surprised leadership: while video production and editorial consulting dominated, several high-value outlier projects in design and development pointed to underleveraged synergies. Most notably, Scoop confirmed the reliability and completeness of the quote system—meaning leadership could make decisions with confidence, free from concerns about data gaps or reporting lag.
Armed with Scoop’s synthesized insights, the agency immediately prioritized outreach to their most frequent and highest-value clients for upsell opportunities. Leaders directed marketing to double down on video production and editorial consulting, confirming these as principal revenue drivers. Finance teams, now confident in quarter-over-quarter revenue tracking, adjusted forecasts to reflect upcoming project surges flagged for early next year. Plans were set to segment prospecting by service line performance and to design targeted offers, particularly where client history suggested untapped growth. Follow-up analysis is scheduled for the next sales cycle, using Scoop’s agentic AI to ingest new quote and conversion data, refine segmentation, and further optimize resource allocation.